stack es ml start-trained-model-deployment cli command

elastic stack es ml start-trained-model-deployment \
  --model-id <model-id> \
  [options]
		

Start a trained model deployment.

Behaviour flags:

--dry-run — validate all inputs and exit without performing any action

--model-id string required

The unique identifier of the trained model. Currently, only PyTorch models are supported.

--cache-size string

The inference cache size (in memory outside the JVM heap) per node for the model. The default value is the same size as the model_size_bytes. To disable the cache, 0b can be provided.

--deployment-id string

A unique identifier for the deployment of the model.

--number-of-allocations number

The number of model allocations on each node where the model is deployed. All allocations on a node share the same copy of the model in memory but use a separate set of threads to evaluate the model. Increasing this value generally increases the throughput. If this setting is greater than the number of hardware threads it will automatically be changed to a value less than the number of hardware threads. If adaptive_allocations is enabled, do not set this value, because it’s automatically set.

--priority enum

The deployment priority

Values: normal, low

--queue-capacity number

Specifies the number of inference requests that are allowed in the queue. After the number of requests exceeds this value, new requests are rejected with a 429 error.

--threads-per-allocation number

Sets the number of threads used by each model allocation during inference. This generally increases the inference speed. The inference process is a compute-bound process; any number greater than the number of available hardware threads on the machine does not increase the inference speed. If this setting is greater than the number of hardware threads it will automatically be changed to a value less than the number of hardware threads.

--timeout string

Specifies the amount of time to wait for the model to deploy.

--wait-for enum

Specifies the allocation status to wait for before returning.

Values: started, starting, fully_allocated

--adaptive-allocations string

Adaptive allocations configuration. When enabled, the number of allocations is set based on the current load. If adaptive_allocations is enabled, do not set the number of allocations manually.

--error-trace

When set to true Elasticsearch will include the full stack trace of errors when they occur.

--filter-path string

Comma-separated list of filters in dot notation which reduce the response returned by Elasticsearch.

Repeatable: pass --filter-path multiple times to supply more than one value

--human

When set to true will return statistics in a format suitable for humans. For example "exists_time": "1h" for humans and "exists_time_in_millis": 3600000 for computers. When disabled the human readable values will be omitted. This makes sense for responses being consumed only by machines.

--pretty

If set to true the returned JSON will be "pretty-formatted". Only use this option for debugging only.

--input-file string

path to a JSON file to use as command input

--dry-run

validate all inputs and exit without performing any action (preview changes without applying them)

--json

output as JSON